Driven by the growing demand for efficient storage and transmission of visual information, significant progress has been made in image and video compression technologies during the past decades. However, most of the current image compression standards and technologies are, to a large extent, still data-driven. These known compression standards and technologies are designed to minimize the overall pixel-wise distortion, such as mean square error. Since data-driven methodology do not take full advantage of the rich information in the image data, these existing methods usually lead to low or moderate data reduction and have no guarantee for good perceptual/subjective quality of reconstructed images, especially in low bit-rate scenarios and for low-bandwidth image transmission applications. Regarding the applicability and importance of perceptual/subjective quality of reconstructed images, an example would be a scenario in which before a missile hits its designated target, an image of the target is fed back to a human operator for a determination. In this scenario, it is critical to achieve a good image perceptual/subjective quality rather than an objective one under the communication bandwidth constraint because the human operator has to rely on the visual perception of the image content for the final decision making.
Attempting to address the limitations imposed by the data-driven compression methods, there have been some research efforts in developing the so-called feature-based image compression techniques. These feature-based image compression (FBIC) approaches strive to achieve higher coding efficiency and better perceptual quality by identifying and encoding different features in image data according to the properties of human visual system (HVS). The image model often used characterizes the image content as regions surrounded by contours. With such an image model, the FBIC techniques can be roughly categorized into two classes. The first is usually called region-based approach, which partitions the image into homogeneous closed regions and utilizes the uniformity of each of the segmented regions. The second class, often named as contour-based approach, extract and encode edge segments (as discontinuities between regions) and use them to reconstruct an approximation of the original image. In addition, there were also efforts to develop hybrid coding schemes by combining the classic transformed-based and the feature-based approaches.
As can be seen, certain image transmission/communication applications with ultra-low bandwidth and runtime constraints require the maximization of the perceptual/subjective quality of the reconstructed image. The classic transform-based techniques are unsuitable in this context and technological improvement is desired. Furthermore, none of the known feature-based compression techniques can be directly applied to satisfy the bandwidth constraint or meet the runtime requirements, primarily due to the lack of efficient coding of the visually significant features and the high computational complexity exhibited by the existing algorithms.
Therefore to address the above inefficiency, it is desirous to have an improved, practical, and efficient image compression solution, to enable ultra-low bandwidth image communication/transmission with enhanced subjective/perception quality.